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Advanced Remote Sensing Technologies for Disaster Monitoring

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 19022

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Special Issue Editors

Dr. Seonyoung Park

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Guest Editor

Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Interests: drought; evapotranspiration; disaster; environmental impact assessment; soil and water conservation; remote sensing and GIS; agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Jong-min Yeom

E-Mail Website
Guest Editor

Satellite Application Division, Korea Aerospace Research Institute, 169-84 Gwahak-ro, Daejeon 34133, Korea
Interests: application of remote sensing; integration of satellite images into deep-learning models; remote sensing of ecological resources in land surface
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For the last decade or so, there has been intense research activity regarding the exploitation of remote sensing technologies in disasters such as drought, extreme temperatures, earthquakes, cyclones, flooding, landslides, wildfires, etc. Climate change is affecting the occurrences of disasters, resulting in the higher vulnerability of regions to severe events. It is important to prevent, mitigate, and recover from disasters by monitoring these disasters using enhanced technologies. Remote sensing is one of such technologies that is suitable to effectively collect data on a large scale with varied spatial, spectral, and temporal resolutions. Many satellite’s data has been employed to monitor disasters, identify the damage of disasters, and assess the recovery of disaster.

This Special Issue invites state-of-the-art research on disaster monitoring using satellite remote sensing data. In this Special Issue, we expect to introduce various studies covering remote sensing technologies that can be applied in disaster monitoring.

Dr. Seonyoung Park
Dr. Jong-min Yeom
Guest Editors

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Keywords

Monitoring natural hazards
Landslides and land degradation
Climate change
Land use and land cover change
Typhoon
Droughts
Floods, and floodplains
Earthquakes
Tsunamis
Hazard and vulnerability assessments
Risk mapping
Early warning systems

Published Papers (6 papers)

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Research

16 pages, 6530 KiB

Open AccessArticle

Monitoring Spatiotemporal Evolution of Urban Heat Island Effect and Its Dynamic Response to Land Use/Land Cover Transition in 1987–2016 in Wuhan, China

by Qijiao Xie, Qi Sun and Zhonglu Ouyang

Appl. Sci. 2020, 10(24), 9020; https://doi.org/10.3390/app10249020 - 17 Dec 2020

Cited by 5 | Viewed by 1928

Abstract

Monitoring the relationship between the urban heat island (UHI) effect and land use/land cover (LULC) is of great significance in land use planning to adapt to climate change. However, the dynamic response of the UHI effect to LULC change over space and time has not been deeply studied. In this study, a transfer matrix method was carried out to monitor the class-to-class transitions between different LULC types, as well as those between different NLST (normalized land surface temperature) levels over space and time. The spatiotemporal correlation and dynamic coupling between UHI variation and LULC change from 1987 to 2016 were simulated based on multi-temporal remote sensing data in Wuhan, China. The results showed that high temperature (level V) and sub-high temperature (level IV) were mainly concentrated in construction land, while the majority of low temperature (level I) was distributed in water bodies. During the study period, the most notable changes were the rapid increase in construction land, as well as the continuous shrinkage of farmland and water bodies. The inward transfer of construction land was mainly from farmland and water bodies, with the transferred area of 218.3 km² (69.2%) and 78.9 km² (25.0%). These transitions were mainly responsible for the thermal deterioration in the study area. The transition of farmland to construction land contributed the most (66.3% and 81.6%) to thermal deterioration in the original medium temperature area (level III). The transition of water bodies to construction land was the main driving force in rapidly upgrading NLST level I into level IV (55.8%) and level V (58.6%). These findings provided detailed information for decision support in optimizing land use structure to fight against the thermal deterioration caused by urbanization. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technologies for Disaster Monitoring)

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18 pages, 6998 KiB

Open AccessArticle

SAR and LIDAR Datasets for Building Damage Evaluation Based on Support Vector Machine and Random Forest Algorithms—A Case Study of Kumamoto Earthquake, Japan

by Masoud Hajeb, Sadra Karimzadeh and Masashi Matsuoka

Appl. Sci. 2020, 10(24), 8932; https://doi.org/10.3390/app10248932 - 14 Dec 2020

Cited by 18 | Viewed by 5742

Abstract

The evaluation of buildings damage following disasters from natural hazards is a crucial step in determining the extent of the damage and measuring renovation needs. In this study, a combination of the synthetic aperture radar (SAR) and light detection and ranging (LIDAR) data before and after the earthquake were used to assess the damage to buildings caused by the Kumamoto earthquake. For damage assessment, three variables including elevation difference (ELD) and texture difference (TD) in pre- and post-event LIDAR images and coherence difference (CD) in SAR images before and after the event were considered and their results were extracted. Machine learning algorithms including random forest (RDF) and the support vector machine (SVM) were used to classify and predict the rate of damage. The results showed that ELD parameter played a key role in identifying the damaged buildings. The SVM algorithm using the ELD parameter and considering three damage rates, including D0 and D1 (Negligible to slight damages), D2, D3 and D4 (Moderate to Heavy damages) and D5 and D6 (Collapsed buildings) provided an overall accuracy of about 87.1%. In addition, for four damage rates, the overall accuracy was about 78.1%. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technologies for Disaster Monitoring)

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21 pages, 5352 KiB

Open AccessArticle

Developing a New Hourly Forest Fire Risk Index Based on Catboost in South Korea

by Yoojin Kang, Eunna Jang, Jungho Im, Chungeun Kwon and Sungyong Kim

Appl. Sci. 2020, 10(22), 8213; https://doi.org/10.3390/app10228213 - 19 Nov 2020

Cited by 26 | Viewed by 3881

Abstract

Forest fires can cause enormous damage, such as deforestation and environmental pollution, even with a single occurrence. It takes a lot of effort and long time to restore areas damaged by wildfires. Therefore, it is crucial to know the forest fire risk of a region to appropriately prepare and respond to such disastrous events. The purpose of this study is to develop an hourly forest fire risk index (HFRI) with 1 km spatial resolution using accessibility, fuel, time, and weather factors based on Catboost machine learning over South Korea. HFRI was calculated through an ensemble model that combined an integrated model using all factors and a meteorological model using weather factors only. To confirm the generalized performance of the proposed model, all forest fires that occurred from 2014 to 2019 were validated using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) values through one-year-out cross-validation. The AUC value of HFRI ensemble model was 0.8434, higher than the meteorological model. HFRI was compared with the modified version of Fine Fuel Moisture Code (FFMC) used in the Canadian Forest Fire Danger Rating Systems and Daily Weather Index (DWI), South Korea’s current forest fire risk index. When compared to DWI and the revised FFMC, HFRI enabled a more spatially detailed and seasonally stable forest fire risk simulation. In addition, the feature contribution to the forest fire risk prediction was analyzed through the Shapley Additive exPlanations (SHAP) value of Catboost. The contributing variables were in the order of relative humidity, elevation, road density, and population density. It was confirmed that the accessibility factors played very important roles in forest fire risk modeling where most forest fires were caused by anthropogenic factors. The interaction between the variables was also examined. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technologies for Disaster Monitoring)

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20 pages, 3910 KiB

Open AccessArticle

Rainfall Standard of Disaster Prediction for Agricultural Droughts in S. Korea

by Youngseok Song and Moojong Park

Appl. Sci. 2020, 10(21), 7423; https://doi.org/10.3390/app10217423 - 22 Oct 2020

Viewed by 1866

Abstract

With the climate change adding to the frequency and intensity of natural disasters, drought has devastated large areas of lands in South Korea. Still, the exact beginning and end of the drought is difficult to identify, and this impedes the development and implementation of disaster predictions. Although the drought phenomenon has been well-documented, predictions thereof are limited due to the non-linear and complex temporal fluctuations of the hydrologic factors. Hence, this study set up some reference points for disaster-prediction rainfall based on South Korea’s agricultural drought damage data, to help in drought relief. To set up the proposed reference points for disaster-prediction rainfall, we analyzed rainfall in light of the disaster-prevention relevance to agricultural droughts and the disaster reduction. As an analysis method, rainfall of municipality was calculated through Thiessen’s polygonal method, to apply rainfall weighting value for each rainfall observatory. In addition, the linear regression analysis was applied to suggest the calculation formula for setting the annual disaster reduction rainfall. The results of this study, standard of judgment point for disaster prevention of agricultural drought at the time of disaster management, were analyzed for rainfall for local governments and the whole country. Rather than using various drought indices that are currently developed, policy makers or public servant made suggestions based on rainfall that is most accessible and convenient for judging the timing of agricultural drought. As the disaster-prevention rainfall with agricultural droughts is expected to occur, we established the average annual rainfall of ≤1200 or 100 mm below the preceding year’s average annual rainfall. Moreover, as the disaster-reduction rainfall for agricultural droughts to end, we determined the average monthly rainfall of ≥150 mm. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technologies for Disaster Monitoring)

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10 pages, 2463 KiB

Open AccessArticle

Feasibility Study for Spatial Distribution of Diesel Oil in Contaminated Soils by Laser Induced Fluorescence

by Yanhong Gu, Zhaolu Zuo, Chaoyi Shi and Xueyou Hu

Appl. Sci. 2020, 10(3), 1103; https://doi.org/10.3390/app10031103 - 7 Feb 2020

Cited by 5 | Viewed by 1862

Abstract

Laser induced fluorescence (LIF) technique has been demonstrated as a powerful technology for analyzing the contamination of petroleum due to its excellent attributes of rapid analysis speed and slight sample preparation. This study focuses on the monitoring application of LIF in petroleum hydrocarbon-contaminated soils by establishing the three-dimensional diffusion models. In this paper, to improve the analysis accuracy, the effects of soil matrix difference for fluorescence intensities were considered. In order to validate the practicability of LIF, the longitudinal penetration laws and the lateral diffusion laws of diesel oil in different humidity soils were analyzed. These laws indicate that the longitudinal penetration depth decreases and the lateral diffusion range increases with the increase of soil moisture. Then, the three-dimensional diffusion models were established, the relative standard deviation (RSD) of the predictions for diesel oil in different soil moisture are 5.09%, 9.62%, 7.92%, and the contaminated volumes of soils by diesel oil are 233.90 cm³, 332.70 cm³, and 660.05 cm³, respectively. These results express that the soil moisture extends the extent of diesel-contaminated soils. The present work shows the feasibility of LIF technique for the field monitoring of petroleum. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technologies for Disaster Monitoring)

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13 pages, 4502 KiB

Open AccessArticle

Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model

by Min Ji, Lanfa Liu, Rongchun Zhang and Manfred F. Buchroithner

Appl. Sci. 2020, 10(2), 602; https://doi.org/10.3390/app10020602 - 14 Jan 2020

Cited by 19 | Viewed by 2990

Abstract

The building is an indispensable part of human life which provides a place for people to live, study, work, and engage in various cultural and social activities. People are exposed to earthquakes, and damaged buildings caused by earthquakes are one of the main threats. It is essential to retrieve the detailed information of affected buildings after earthquakes. Very high-resolution satellite imagery plays a key role in retrieving building damage information since it captures imagery quickly and effectively after the disaster. In this paper, the pretrained Visual Geometry Group (VGG)Net model was applied for identifying collapsed buildings induced by the 2010 Haiti earthquake using pre- and post-event remotely sensed space imagery, and the fine-tuned pretrained VGGNet model was compared with the VGGNet model trained from scratch. The effects of dataset augmentation and freezing different intermediate layers were also explored. The experimental results demonstrated that the fine-tuned VGGNet model outperformed the VGGNet model trained from scratch with increasing overall accuracy (OA) from 83.38% to 85.19% and Kappa from 60.69% to 67.14%. By taking advantage of dataset augmentation, OA and Kappa went up to 88.83% and 75.33% respectively, and the collapsed buildings were better recognized with a larger producer accuracy of 86.31%. The present study showed the potential of using the pretrained Convolutional Neural Network (CNN) model to identify collapsed buildings caused by earthquakes using very high-resolution satellite imagery. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technologies for Disaster Monitoring)

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